Personal Assistant Systems
Trump Champions Peace Agreement, Threatens to Resume Bombing If Iran Doesn't Comply
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A Rock Band Went Viral. Then AI Scammers Moved In
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Why You're Seeing a PA or NP--But Not a Doctor
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Breaking Down the Thrilling Ending of Widow's Bay
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How to Make an Impact in the AI Economy
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Energy Independence is Becoming Solar's Strongest Selling Point
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Read the Full Text of the 14-Point Draft Agreement Between the U.S. and Iran
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Frank Wills and the Importance of Ordinary Americans Doing the Right Thing
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G7 Leaders Call For 'Immediate Cease-Fire' in Lebanon as They Welcome U.S.-Iran Peace Deal
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Integrating Drug Substructures and Longitudinal Electronic Health Records for Personalized Drug Recommendation
Drug recommendation systems aim to identify optimal drug combinations for patient care, balancing therapeutic efficacy and safety. Advances in large-scale longitudinal EHRs have enabled learning-based approaches that leverage patient histories such as diagnoses, procedures, and previously prescribed drugs, to model complex patient-drug relationships. Yet, many existing solutions overlook standard clinical practices that favor certain drugs for specific conditions and fail to fully integrate the influence of molecular substructures on drug efficacy and safety. In response, we propose SubRec, a unified framework that integrates representation learning across both patient and drug spaces. Specifically, SubRec introduces a conditional information bottleneck to extract core drug substructures most relevant to patient conditions, thereby enhancing interpretability and clinical alignment. Meanwhile, an adaptive vector quantization mechanism is designed to generate patient-drug interaction patterns into a condition-aware codebook which reuses clinically meaningful patterns, reduces training overhead, and provides a controllable latent space for recommendation. Crucially, the synergy between condition-specific substructure learning and discrete patient prototypes allows SubRec to make accurate and personalized drug recommendations. Experimental results on the real-world MIMICIII and IV demonstrate our model's advantages. The source code is available at https://DrugRecommendation/.